Error-minimizing dead zone for basis function networks

The incorporation of dead zones in the error signal of basis function networks avoids the networks´ overtraining and guarantees the convergence of the normalized least mean square (LMS) algorithm and related algorithms. A new so-called error-minimizing dead zone is presented providing the least a posteriori error out of the set of all convergence assuring dead zones. A general convergence proof is developed for LMS algorithms with dead zones, and the error-minimizing dead zone is derived from the resulting convergence condition. The performance is compared with the performance of classical dead zones